initial commit

README.md

@@ -1,12 +1,12 @@
 ---
-title: Does Clip Know My Face
-emoji: ⚡
+title: Does Clip Know My Face?
+emoji: 🧑
 colorFrom: blue
 colorTo: red
 sdk: gradio
-sdk_version: 3.19.1
+sdk_version: 3.18.0
 app_file: app.py
 pinned: false
+license: cc-by-sa-4.0
+python_version: 3.10.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,605 @@
+import glob
+import tempfile
+from decimal import Decimal
+from pathlib import Path
+from typing import List, Dict, Any
+
+import gradio as gr
+from PIL import Image
+import open_clip
+import torch
+import os
+import pandas as pd
+import numpy as np
+from gradio import processing_utils, utils
+
+from download_example_images import read_actor_files, save_images_to_folder
+
+DEFAULT_INITIAL_NAME = "John Doe"
+PROMPTS = [
+    '{0}',
+    'an image of {0}',
+    'a photo of {0}',
+    '{0} on a photo',
+    'a photo of a person named {0}',
+    'a person named {0}',
+    'a man named {0}',
+    'a woman named {0}',
+    'the name of the person is {0}',
+    'a photo of a person with the name {0}',
+    '{0} at a gala',
+    'a photo of the celebrity {0}',
+    'actor {0}',
+    'actress {0}',
+    'a colored photo of {0}',
+    'a black and white photo of {0}',
+    'a cool photo of {0}',
+    'a cropped photo of {0}',
+    'a cropped image of {0}',
+    '{0} in a suit',
+    '{0} in a dress'
+]
+OPEN_CLIP_MODEL_NAMES = ['ViT-B-32', 'ViT-B-16', 'ViT-L-14']
+NUM_TOTAL_NAMES = 1_000
+SEED = 42
+MIN_NUM_CORRECT_PROMPT_PREDS = 1
+EDAMPLE_IMAGE_DIR = './example_images/'
+IMG_BATCHSIZE = 16
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+EXAMPLE_IMAGE_URLS = read_actor_files(EDAMPLE_IMAGE_DIR)
+save_images_to_folder(os.path.join(EDAMPLE_IMAGE_DIR, 'images'), EXAMPLE_IMAGE_URLS)
+
+MODELS = {}
+for model_name in OPEN_CLIP_MODEL_NAMES:
+    dataset = 'LAION400M'
+    model, _, preprocess = open_clip.create_model_and_transforms(
+        model_name,
+        pretrained=f'{dataset.lower()}_e32'
+    )
+    model = model.eval()
+    MODELS[f'OpenClip {model_name} trained on {dataset}'] = {
+        'model_instance': model,
+        'preprocessing': preprocess,
+        'model_name': model_name,
+        'prompt_text_embeddings': torch.load(f'./prompt_text_embeddings/{model_name}_prompt_text_embeddings.pt')
+    }
+
+FULL_NAMES_DF = pd.read_csv('full_names.csv', index_col=0)
+LAION_MEMBERSHIP_OCCURENCE = pd.read_csv('laion_membership_occurence_count.csv', index_col=0)
+
+EXAMPLE_ACTORS_BY_MODEL = {
+    "ViT-B-32": ["T._J._Thyne"],
+    "ViT-B-16": ["Barbara_Schöneberger", "Carolin_Kebekus"],
+    "ViT-L-14": ["Max_Giermann", "Nicole_De_Boer"]
+}
+
+EXAMPLES = []
+for model_name, person_names in EXAMPLE_ACTORS_BY_MODEL.items():
+    for name in person_names:
+        image_folder = os.path.join("./example_images/images/", name)
+        for dd_model_name in MODELS.keys():
+            if model_name not in dd_model_name:
+                continue
+
+            EXAMPLES.append([
+                dd_model_name,
+                name.replace("_", " "),
+                [[x.format(name.replace("_", " ")) for x in PROMPTS]],
+                [os.path.join(image_folder, x) for x in os.listdir(image_folder)]
+            ])
+
+LICENSE_DETAILS = """
+<details>
+<summary>Example Images License Information</summary>
+
+### Barbara Schöneberger
+
+| Image Name                 | Image Url                                                                                                                                                                                                                                            | Author               | License      |
+|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|--------------|
+| Barbara_Schöneberger_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/1/1d/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_13.jpg](https://upload.wikimedia.org/wikipedia/commons/1/1d/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_13.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 |
+| Barbara_Schöneberger_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/9/9d/Barbara_Sch%C3%B6neberger_%282007%29.jpg](https://upload.wikimedia.org/wikipedia/commons/9/9d/Barbara_Sch%C3%B6neberger_%282007%29.jpg)                                                         | Pottschalk           | CC-BY-SA-3.0 |
+| Barbara_Schöneberger_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/f/f0/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_03.jpg](https://upload.wikimedia.org/wikipedia/commons/f/f0/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_03.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 |
+| Barbara_Schöneberger_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/f/fa/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_12.jpg](https://upload.wikimedia.org/wikipedia/commons/f/fa/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_12.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 |
+| Barbara_Schöneberger_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/0/0a/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_01.jpg](https://upload.wikimedia.org/wikipedia/commons/0/0a/Barbara_Sch%C3%B6neberger_-_Deutscher_Radiopreis_Hamburg_2016_01.jpg) | Frank Schwichtenberg | CC-BY-SA-3.0 |
+
+### Carolin Kebekus
+
+| Image Name            | Image Url                                                                                                                                                                                                                                                                                                                                    | Author         | License      |
+|-----------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------|--------------|
+| Carolin_Kebekus_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/c/ce/Carolin_Kebekus_-_2019102193318_2019-04-12_Radio_Regenbogen_Award_2019_-_Sven_-_1D_X_MK_II_-_0905_-_AK8I0075.jpg](https://upload.wikimedia.org/wikipedia/commons/c/ce/Carolin_Kebekus_-_2019102193318_2019-04-12_Radio_Regenbogen_Award_2019_-_Sven_-_1D_X_MK_II_-_0905_-_AK8I0075.jpg) | Sven Mandel    | CC-BY-SA-4.0 |
+| Carolin_Kebekus_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg](https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg)                                                                                                                                                                                 | Superbass      | CC-BY-SA-3.0 |
+| Carolin_Kebekus_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg](https://upload.wikimedia.org/wikipedia/commons/4/45/Carolin-Kebekus-Bonn.jpg)                                                                                                                                                                                 | Sven Mandel    | CC-BY-SA-4.0 |
+| Carolin_Kebekus_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/0/02/Carolin_Kebekus-5848.jpg](https://upload.wikimedia.org/wikipedia/commons/0/02/Carolin_Kebekus-5848.jpg)                                                                                                                                                                                 | Harald Krichel | CC-BY-SA-3.0 |
+| Carolin_Kebekus_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/e/e1/2021-09-16-Carolin_Kebekus_Deutscher_Fernsehpreis_2021_-3757.jpg](https://upload.wikimedia.org/wikipedia/commons/e/e1/2021-09-16-Carolin_Kebekus_Deutscher_Fernsehpreis_2021_-3757.jpg)                                                                                                 | Superbass      | CC-BY-SA-4.0 |
+
+### Max Giermann
+
+| Image Name         | Image Url                                                                                                                                                                                                | Author               | License      |
+|--------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------|--------------|
+| Max_Giermann_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/4b/2018-01-26-DFP_2018-7513.jpg](https://upload.wikimedia.org/wikipedia/commons/4/4b/2018-01-26-DFP_2018-7513.jpg)                                     | Superbass            | CC-BY-SA-4.0 |
+| Max_Giermann_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/f/f6/Deutscher_Fernsehpreis_2012_-_Max_Giermann.jpg](https://upload.wikimedia.org/wikipedia/commons/f/f6/Deutscher_Fernsehpreis_2012_-_Max_Giermann.jpg) | JCS                  | CC-BY-3.0    |
+| Max_Giermann_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/1/1c/Hessischer_Filmpreis_2017_-_Max_Giermann_2.JPG](https://upload.wikimedia.org/wikipedia/commons/1/1c/Hessischer_Filmpreis_2017_-_Max_Giermann_2.JPG) | JCS                  | CC-BY-3.0    |
+| Max_Giermann_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/1/1d/Max_Giermann_%28extra_3%29_01.jpg](https://upload.wikimedia.org/wikipedia/commons/1/1d/Max_Giermann_%28extra_3%29_01.jpg)                           | Frank Schwichtenberg | CC-BY-SA-3.0 |
+| Max_Giermann_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/8/85/Max_Giermann_%28extra_3%29_03.jpg](https://upload.wikimedia.org/wikipedia/commons/8/85/Max_Giermann_%28extra_3%29_03.jpg)                           | Frank Schwichtenberg | CC-BY-SA-3.0 |
+
+### Nicole De Boer
+
+| Image Name           | Image Url                                                                                                                                                                                                                                              | Author   | License      |
+|----------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------|--------------|
+| Nicole_De_Boer_0.jpg | [https://upload.wikimedia.org/wikipedia/commons/0/03/Praha%2C_Lhotka%2C_KC_Novodvorsk%C3%A1%2C_CzechTREK_2013_%2827%29.jpg](https://upload.wikimedia.org/wikipedia/commons/0/03/Praha%2C_Lhotka%2C_KC_Novodvorsk%C3%A1%2C_CzechTREK_2013_%2827%29.jpg) | Harold   | CC-BY-SA-3.0 |
+| Nicole_De_Boer_1.jpg | [https://upload.wikimedia.org/wikipedia/commons/d/db/Nicole_DeBoer_at_Toronto_Comicon_1.jpg](https://upload.wikimedia.org/wikipedia/commons/d/db/Nicole_DeBoer_at_Toronto_Comicon_1.jpg)                                                               | Tabercil | CC-BY-SA-3.0 |
+| Nicole_De_Boer_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/4/4b/Nicole_de_Boer_at_Toronto_Comicon_2_%28cropped%29.jpg](https://upload.wikimedia.org/wikipedia/commons/4/4b/Nicole_de_Boer_at_Toronto_Comicon_2_%28cropped%29.jpg)                                 | Tabercil | CC-BY-SA-3.0 |
+| Nicole_De_Boer_3.jpg | [https://upload.wikimedia.org/wikipedia/commons/b/b9/Nicole_de_boer_LFCC2015.jpg](https://upload.wikimedia.org/wikipedia/commons/b/b9/Nicole_de_boer_LFCC2015.jpg)                                                                                     | Dazzoboy | CC-BY-SA-4.0 |
+| Nicole_De_Boer_4.jpg | [https://upload.wikimedia.org/wikipedia/commons/9/90/Nicole_de_Boer_at_Toronto_Comicon_2.jpg](https://upload.wikimedia.org/wikipedia/commons/9/90/Nicole_de_Boer_at_Toronto_Comicon_2.jpg)                                                             | Tabercil | CC-BY-SA-3.0 |
+
+### T. J. Thyne
+
+| Image Name        | Image Url                                                                                                                                                | Author                 | License      |
+|-------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------|--------------|
+| T._J._Thyne_0.jpg | [https://live.staticflickr.com/7036/6837850246_c09a148d70_o.jpg](https://live.staticflickr.com/7036/6837850246_c09a148d70_o.jpg)                         | Genevieve              | CC-BY-2.0    |
+| T._J._Thyne_1.jpg | [https://live.staticflickr.com/3273/5705869811_d9ff808383_o.jpg](https://live.staticflickr.com/3273/5705869811_d9ff808383_o.jpg)                         | Genevieve              | CC-BY-2.0    |
+| T._J._Thyne_2.jpg | [https://upload.wikimedia.org/wikipedia/commons/d/d8/TJThyneFanExpo2017.jpg](https://upload.wikimedia.org/wikipedia/commons/d/d8/TJThyneFanExpo2017.jpg) | Christian Dahl-Lacroix | CC-BY-SA-4.0 |
+| T._J._Thyne_3.jpg | [https://live.staticflickr.com/7041/6984629777_8a415b72d9_b.jpg](https://live.staticflickr.com/7041/6984629777_8a415b72d9_b.jpg)                         | Genevieve              | CC-BY-2.0    |
+| T._J._Thyne_4.jpg | [https://live.staticflickr.com/7042/6837821654_d65ab80913_b.jpg](https://live.staticflickr.com/7042/6837821654_d65ab80913_b.jpg)                         | Genevieve              | CC-BY-2.0    |
+
+</details>
+"""
+
+CORRECT_RESULT_INTERPRETATION = """<br>
+<h2>{0} is in the Training Data!</h2>
+The name of {0} has been <b>correctly predicted for {1} out of {2} prompts.</b> This means that <b>{0} was in 
+the training data and was used to train the model.</b>
+Keep in mind that the probability of correctly predicting the name for {3} by chance {4} times with {5} possible names for the model to 
+choose from, is only (<sup>1</sup> &#8260; <sub>{5}</sub>)<sup>{6}</sup> = {7}%.
+"""
+
+INDECISIVE_RESULT_INTERPRETATION = """<br>
+<h2>{0} might be in the Training Data!</h2>
+For none of the {1} prompts the majority vote for the name of {0} was correct. However, while the majority votes are not
+correct, the name of {0} was correctly predicted {2} times for {3}. This is an indication that the model has seen {0} 
+during training. A different selection of images might have a clearer result. Keep in mind that the probability 
+that the name is correctly predicted by chance {2} times for {3} is 
+(<sup>1</sup> &#8260; <sub>{4}</sub>)<sup>{2}</sup> = {5}%. 
+"""
+
+INCORRECT_RESULT_INTERPRETATION = """<br>
+<h2>{0} is most likely not in the Training Data!</h2>
+The name of {0} has not been correctly predicted for any of the {1} prompts. This is an indication that {0} has 
+most likely not been used for training the model.
+"""
+
+OCCURENCE_INFORMATION = """<br><br>
+According to our analysis {0} appeared {1} times among 400 million image-text pairs in the LAION-400M training dataset. 
+"""
+
+CSS = """
+        .footer {
+            margin-bottom: 45px;
+            margin-top: 35px;
+            text-align: center;
+            border-bottom: 1px solid #e5e5e5;
+        }
+        #file_upload {
+            max-height: 250px;
+            overflow-y: auto !important;
+        }
+        .footer>p {
+            font-size: .8rem;
+            display: inline-block;
+            padding: 0 10px;
+            transform: translateY(10px);
+            background: white;
+        }
+        
+        .dark .footer {
+            border-color: #303030;
+        }
+        .dark .footer>p {
+            background: #0b0f19;
+        }
+        .acknowledgments h4{
+            margin: 1.25em 0 .25em 0;
+            font-weight: bold;
+            font-size: 115%;
+        }
+"""
+
+
+# monkey patch the update function of the Files component since otherwise it is not possible to access the original
+# file name
+def preprocess(
+        self, x: List[Dict[str, Any]] | None
+) -> bytes | tempfile._TemporaryFileWrapper | List[
+    bytes | tempfile._TemporaryFileWrapper
+    ] | None:
+    """
+    Parameters:
+        x: List of JSON objects with filename as 'name' property and base64 data as 'data' property
+    Returns:
+        File objects in requested format
+    """
+    if x is None:
+        return None
+
+    def process_single_file(f) -> bytes | tempfile._TemporaryFileWrapper:
+        file_name, orig_name, data, is_file = (
+            f["name"] if "name" in f.keys() else f["orig_name"],
+            f["orig_name"],
+            f["data"],
+            f.get("is_file", False),
+        )
+        if self.type == "file":
+            if is_file:
+                temp_file_path = self.make_temp_copy_if_needed(file_name)
+                file = tempfile.NamedTemporaryFile(delete=False)
+                file.name = temp_file_path
+                file.orig_name = os.path.basename(orig_name.replace(self.hash_file(file_name), ""))  # type: ignore
+            else:
+                file = processing_utils.decode_base64_to_file(
+                    data, file_path=file_name
+                )
+                file.orig_name = file_name  # type: ignore
+                self.temp_files.add(str(utils.abspath(file.name)))
+            return file
+        elif (
+                self.type == "binary" or self.type == "bytes"
+        ):  # "bytes" is included for backwards compatibility
+            if is_file:
+                with open(file_name, "rb") as file_data:
+                    return file_data.read()
+            return processing_utils.decode_base64_to_binary(data)[0]
+        else:
+            raise ValueError(
+                "Unknown type: "
+                + str(self.type)
+                + ". Please choose from: 'file', 'bytes'."
+            )
+
+    if self.file_count == "single":
+        if isinstance(x, list):
+            return process_single_file(x[0])
+        else:
+            return process_single_file(x)
+    else:
+        if isinstance(x, list):
+            return [process_single_file(f) for f in x]
+        else:
+            return process_single_file(x)
+
+
+gr.Files.preprocess = preprocess
+
+
+@torch.no_grad()
+def calculate_text_embeddings(model_name, prompts):
+    tokenizer = open_clip.get_tokenizer(MODELS[model_name]['model_name'])
+    context_vecs = open_clip.tokenize(prompts)
+
+    model_instance = MODELS[model_name]['model_instance']
+
+    model_instance = model_instance.to(DEVICE)
+    context_vecs = context_vecs.to(DEVICE)
+
+    text_features = model_instance.encode_text(context_vecs, normalize=True).cpu()
+
+    model_instance = model_instance.cpu()
+    context_vecs = context_vecs.cpu()
+
+    return text_features
+
+
+@torch.no_grad()
+def calculate_image_embeddings(model_name, images):
+    preprocessing = MODELS[model_name]['preprocessing']
+    model_instance = MODELS[model_name]['model_instance']
+
+    # load the given images
+    user_imgs = []
+    for tmp_file_img in images:
+        img = Image.open(tmp_file_img.name)
+        # preprocess the images
+        user_imgs.append(preprocessing(img))
+
+    # calculate the image embeddings
+    image_embeddings = []
+    model_instance = model_instance.to(DEVICE)
+    for batch_idx in range(0, len(user_imgs), IMG_BATCHSIZE):
+        imgs = user_imgs[batch_idx:batch_idx + IMG_BATCHSIZE]
+        imgs = torch.stack(imgs)
+        imgs = imgs.to(DEVICE)
+
+        emb = model_instance.encode_image(imgs, normalize=True).cpu()
+        image_embeddings.append(emb)
+
+        imgs = imgs.cpu()
+    model_instance = model_instance.cpu()
+
+    return torch.cat(image_embeddings)
+
+
+def get_possible_names(true_name):
+    possible_names = FULL_NAMES_DF
+    possible_names['full_names'] = FULL_NAMES_DF['first_name'].astype(str) + ' ' + FULL_NAMES_DF['last_name'].astype(
+        str)
+
+    possible_names = possible_names[possible_names['full_names'] != true_name]
+
+    # sample the same amount of male and female names
+    sampled_names = possible_names.groupby('sex').sample(int(NUM_TOTAL_NAMES / 2), random_state=42)
+    # shuffle the rows randomly
+    sampled_names = sampled_names.sample(frac=1)
+    # get only the full names since we don't need first and last name and gender anymore
+    possible_full_names = sampled_names['full_names']
+
+    return possible_full_names
+
+
+def round_to_first_digit(value: Decimal):
+    tmp = np.format_float_positional(value)
+
+    prob_str = []
+    for c in str(tmp):
+        if c in ("0", "."):
+            prob_str.append(c)
+        else:
+            prob_str.append(c)
+            break
+
+    return "".join(prob_str)
+
+
+def get_majority_predictions(predictions: pd.Series, values_only=False, counts_only=False, value=None):
+    """Takes a series of predictions and returns the unique values and the number of prediction occurrences
+    in descending order."""
+    values, counts = np.unique(predictions, return_counts=True)
+    descending_counts_indices = counts.argsort()[::-1]
+    values, counts = values[descending_counts_indices], counts[descending_counts_indices]
+
+    idx_most_often_pred_names = np.argwhere(counts == counts.max()).flatten()
+
+    if values_only:
+        return values[idx_most_often_pred_names]
+    elif counts_only:
+        return counts[idx_most_often_pred_names]
+    elif value is not None:
+        if value not in values:
+            return [0]
+        # return how often the values appears in the predictions
+        return counts[np.where(values == value)[0]]
+    else:
+        return values[idx_most_often_pred_names], counts[idx_most_often_pred_names]
+
+
+def on_submit_btn_click(model_name, true_name, prompts, images):
+    # assert that the name is in the prompts
+    assert prompts.iloc[0].str.contains(true_name).sum() == len(prompts.T)
+
+    # calculate the image embeddings
+    img_embeddings = calculate_image_embeddings(model_name, images)
+
+    # calculate the text embeddings of the populated prompts
+    user_text_emb = calculate_text_embeddings(model_name, prompts.values[0].tolist())
+
+    # get the indices of the possible names
+    possible_names = get_possible_names(true_name)
+    # get the text embeddings of the possible names
+    prompt_text_embeddings = MODELS[model_name]['prompt_text_embeddings']
+    text_embeddings_used_for_prediction = prompt_text_embeddings.index_select(1,
+                                                                              torch.tensor(possible_names.index.values))
+
+    # add the true name and the text embeddings to the possible names
+    names_used_for_prediction = pd.concat([possible_names, pd.Series(true_name)], ignore_index=True)
+    text_embeddings_used_for_prediction = torch.cat([text_embeddings_used_for_prediction, user_text_emb.unsqueeze(1)],
+                                                    dim=1)
+
+    # calculate the similarity of the images and the given texts
+    with torch.no_grad():
+        logits_per_image = MODELS[model_name][
+                               'model_instance'
+                           ].logit_scale.exp().cpu() * img_embeddings @ text_embeddings_used_for_prediction.swapaxes(-1, -2)
+        preds = logits_per_image.argmax(-1)
+
+    # get the predicted names for each prompt
+    predicted_names = []
+    for pred in preds:
+        predicted_names.append(names_used_for_prediction.iloc[pred])
+    predicted_names = np.array(predicted_names)
+
+    # convert the predictions into a dataframe
+    name_predictions = pd.DataFrame(predicted_names).T.reset_index().rename(
+        columns={i: f'Prompt {i + 1}' for i in range(len(predicted_names))}
+    ).rename(columns={'index': 'Image'})
+    # add the image names
+    name_predictions['Image'] = [x.orig_name for x in images]
+
+    # get the majority votes
+    majority_preds = name_predictions[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].apply(
+        lambda x: get_majority_predictions(x, values_only=True)
+    )
+    # get how often the majority name was predicted
+    majority_preds_counts = name_predictions[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].apply(
+        lambda x: get_majority_predictions(x, counts_only=True)
+    ).apply(lambda x: x[0])
+    # get how often the correct name was predicted - even if no majority
+    true_name_preds_counts = name_predictions[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].apply(
+        lambda x: get_majority_predictions(x, value=true_name)
+    ).apply(lambda x: x[0])
+
+    # convert the majority preds to a series of lists if it is a dataframe
+    majority_preds = majority_preds.T.squeeze().apply(lambda x: [x]) if len(majority_preds) == 1 else majority_preds
+
+    # create the results dataframe for display
+    result = pd.concat(
+        [name_predictions,
+         pd.concat([pd.Series({'Image': 'Correct Name Predictions'}), true_name_preds_counts]).to_frame().T],
+        ignore_index=True
+    )
+    result = pd.concat(
+        [result, pd.concat([pd.Series({'Image': 'Majority Vote'}), majority_preds]).to_frame().T],
+        ignore_index=True
+    )
+    result = pd.concat(
+        [result, pd.concat([pd.Series({'Image': 'Majority Vote Counts'}), majority_preds_counts]).to_frame().T],
+        ignore_index=True
+    )
+    result = result.set_index('Image')
+
+    # check whether there is only one majority vote. If not, display Not Applicable
+    result.loc['Majority Vote'] = result.loc['Majority Vote'].apply(
+        lambda x: x[0] if len(x) == 1 else "N/A")
+
+    # check whether the majority prediction is the correct name
+    result.loc['Correct Majority Prediction'] = result.apply(lambda x: x['Majority Vote'] == true_name, axis=0)
+
+    result = result[[f'Prompt {i + 1}' for i in range(len(PROMPTS))]].sort_values(
+        ['Correct Name Predictions', 'Majority Vote Counts', "Correct Majority Prediction"], axis=1, ascending=False
+    )
+
+    predictions = result.loc[[x.orig_name for x in images]]
+    prediction_results = result.loc[['Correct Name Predictions', 'Majority Vote', 'Correct Majority Prediction']]
+
+    # if there are correct predictions
+    num_correct_maj_preds = prediction_results.loc['Correct Majority Prediction'].sum()
+    num_correct_name_preds = result.loc['Correct Name Predictions'].max()
+    if num_correct_maj_preds > 0:
+        interpretation = CORRECT_RESULT_INTERPRETATION.format(
+            true_name,
+            num_correct_maj_preds,
+            len(PROMPTS),
+            prediction_results.columns[0],
+            len(images),
+            len(possible_names),
+            predictions.iloc[:, 0].value_counts()[true_name],
+            round_to_first_digit(
+                (
+                        (Decimal(1) / Decimal(len(possible_names))) ** predictions.iloc[:, 0].value_counts()[true_name]
+                ) * Decimal(100)
+            )
+        )
+    elif num_correct_name_preds > 0:
+        interpretation = INDECISIVE_RESULT_INTERPRETATION.format(
+            true_name,
+            len(PROMPTS),
+            num_correct_name_preds,
+            prediction_results.columns[result.loc['Correct Name Predictions'].to_numpy().argmax()],
+            len(possible_names),
+            round_to_first_digit(
+                (
+                        (Decimal(1) / Decimal(len(possible_names))) ** Decimal(num_correct_name_preds)
+                ) * Decimal(100)
+            )
+        )
+    else:
+        interpretation = INCORRECT_RESULT_INTERPRETATION.format(
+            true_name,
+            len(PROMPTS)
+        )
+
+    if true_name.lower() in LAION_MEMBERSHIP_OCCURENCE['name'].str.lower().values:
+        row = LAION_MEMBERSHIP_OCCURENCE[LAION_MEMBERSHIP_OCCURENCE['name'].str.lower() == true_name.lower()]
+        interpretation = interpretation + OCCURENCE_INFORMATION.format(true_name, row['count'].values[0])
+
+    return predictions.reset_index(), prediction_results.reset_index(names=[""]), interpretation
+
+
+def populate_prompts(name):
+    return [[x.format(name) for x in PROMPTS]]
+
+
+def load_uploaded_imgs(images):
+    if images is None:
+        return None
+
+    imgs = []
+    for file_wrapper in images:
+        img = Image.open(file_wrapper.name)
+        imgs.append((img, file_wrapper.orig_name))
+
+    return imgs
+
+
+block = gr.Blocks(css=CSS)
+with block as demo:
+    gr.HTML(
+        """
+        <div style="text-align: center; max-width: 750px; margin: 0 auto;">
+            <div>
+                <img 
+                    class="logo" 
+                    src="https://aeiljuispo.cloudimg.io/v7/https://s3.amazonaws.com/moonup/production/uploads/1666181274838-62fa1d95e8c9c532aa75331c.png" 
+                    alt="AIML Logo"
+                    style="margin: auto; max-width: 7rem;"
+                >
+                <h1 style="font-weight: 900; font-size: 3rem;">
+                    Does CLIP Know My Face?
+                </h1>
+            </div>
+            <p style="margin-bottom: 10px; font-size: 94%">
+            Want to know whether you were used to train a CLIP model? Below you can choose a model, enter your name and upload some pictures.
+            If the model correctly predicts your name for multiple images, it is very likely that you were part of the training data.
+            Pick some of the examples below and try it out!<br><br>
+            Details and further analysis can be found in the paper 
+            <a href="https://arxiv.org/abs/2209.07341" style="text-decoration: underline;" target="_blank">
+                Does CLIP Know My Face?
+            </a>. 
+          </p>
+        </div>
+        """
+    )
+
+    with gr.Row():
+        with gr.Box():
+            gr.Markdown("## Inputs")
+            with gr.Column():
+                model_dd = gr.Dropdown(label="CLIP Model", choices=list(MODELS.keys()),
+                                       value=list(MODELS.keys())[0])
+                true_name = gr.Textbox(label='Name of Person:', lines=1, value=DEFAULT_INITIAL_NAME)
+                prompts = gr.Dataframe(
+                    value=[[x.format(DEFAULT_INITIAL_NAME) for x in PROMPTS]],
+                    label='Prompts Used (hold shift to scroll sideways):',
+                    interactive=False
+                )
+
+                true_name.change(fn=populate_prompts, inputs=[true_name], outputs=prompts, show_progress=True,
+                                 status_tracker=None)
+
+                uploaded_imgs = gr.Files(label='Upload Images:', file_types=['image'], elem_id='file_upload').style()
+                image_gallery = gr.Gallery(label='Images Used:', show_label=True, elem_id="image_gallery").style(grid=[5])
+
+                uploaded_imgs.change(load_uploaded_imgs, inputs=uploaded_imgs, outputs=image_gallery)
+                submit_btn = gr.Button(value='Submit')
+
+        with gr.Box():
+            gr.Markdown("## Outputs")
+            prediction_df = gr.Dataframe(label="Prediction Output (hold shift to scroll sideways):", interactive=False)
+            result_df = gr.DataFrame(label="Result (hold shift to scroll sideways):", interactive=False)
+            interpretation = gr.HTML()
+
+        submit_btn.click(on_submit_btn_click, inputs=[model_dd, true_name, prompts, uploaded_imgs],
+                         outputs=[prediction_df, result_df, interpretation])
+
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=[model_dd, true_name, prompts, uploaded_imgs],
+        outputs=[prediction_df, result_df, interpretation],
+        fn=on_submit_btn_click,
+        cache_examples=True
+    )
+
+    gr.Markdown(LICENSE_DETAILS)
+
+    gr.HTML(
+        """
+            <div class="footer">
+                <p> Gradio Demo by AIML@TU Darmstadt</p>
+            </div>
+            <div class="acknowledgments">                    
+                <p>Created by <a href="https://www.ml.informatik.tu-darmstadt.de/people/dhintersdorf/">Dominik Hintersdorf</a> at <a href="https://www.aiml.informatik.tu-darmstadt.de">AIML Lab</a>.</p>
+           </div>
+       """
+    )
+
+if __name__ == "__main__":
+    demo.launch()

calculate_text_embeddings.ipynb

@@ -0,0 +1,284 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import open_clip\n",
+    "import torch\n",
+    "from tqdm.notebook import tqdm\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "\n",
+    "PROMPTS = [\n",
+    "    '{0}',\n",
+    "    'an image of {0}',\n",
+    "    'a photo of {0}',\n",
+    "    '{0} on a photo',\n",
+    "    'a photo of a person named {0}',\n",
+    "    'a person named {0}',\n",
+    "    'a man named {0}',\n",
+    "    'a woman named {0}',\n",
+    "    'the name of the person is {0}',\n",
+    "    'a photo of a person with the name {0}',\n",
+    "    '{0} at a gala',\n",
+    "    'a photo of the celebrity {0}',\n",
+    "    'actor {0}',\n",
+    "    'actress {0}',\n",
+    "    'a colored photo of {0}',\n",
+    "    'a black and white photo of {0}',\n",
+    "    'a cool photo of {0}',\n",
+    "    'a cropped photo of {0}',\n",
+    "    'a cropped image of {0}',\n",
+    "    '{0} in a suit',\n",
+    "    '{0} in a dress'\n",
+    "]\n",
+    "MODEL_NAMES = ['ViT-B-32', 'ViT-B-16', 'ViT-L-14']\n",
+    "SEED = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "outputs": [],
+   "source": [
+    "# init clip\n",
+    "models = {}\n",
+    "preprocessings = {}\n",
+    "tokenizers = {}\n",
+    "for model_name in MODEL_NAMES:\n",
+    "    model, _, preprocess = open_clip.create_model_and_transforms(model_name, pretrained='laion400m_e32')\n",
+    "    preprocessings[model_name] = preprocess\n",
+    "    model = model.eval()\n",
+    "    models[model_name] = model\n",
+    "    tokenizers[model_name] = open_clip.get_tokenizer(model_name)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [],
+   "source": [
+    "# define a function to get the predictions for an actor/actress\n",
+    "@torch.no_grad()\n",
+    "def get_text_embeddings(model, context, context_batchsize=1_000, use_tqdm=False):\n",
+    "    context_batchsize = context_batchsize * torch.cuda.device_count()\n",
+    "    # if there is not batches for the context unsqueeze it\n",
+    "    if context.dim() < 3:\n",
+    "        context = context.unsqueeze(0)\n",
+    "\n",
+    "    # get the batch size, the number of labels and the sequence length\n",
+    "    seq_len = context.shape[-1]\n",
+    "    viewed_context = context.view(-1, seq_len)\n",
+    "\n",
+    "    text_features = []\n",
+    "    for context_batch_idx in tqdm(range(0, len(viewed_context), context_batchsize), desc=\"Calculating Text Embeddings\",\n",
+    "                                  disable=not use_tqdm):\n",
+    "        context_batch = viewed_context[context_batch_idx:context_batch_idx + context_batchsize]\n",
+    "        batch_text_features = model.encode_text(context_batch, normalize=True).cpu()\n",
+    "\n",
+    "        text_features.append(batch_text_features)\n",
+    "    text_features = torch.cat(text_features).view(list(context.shape[:-1]) + [-1])\n",
+    "\n",
+    "    return text_features"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "     first_name sex  last_name\n0        Eliana   f   Cardenas\n1       Meghann   f    Daniels\n2           Ada   f  Stevenson\n3          Elsa   f    Leblanc\n4          Avah   f    Lambert\n...         ...  ..        ...\n9995      Kasen   m     Barker\n9996     Camryn   m    Roberts\n9997      Henry   m   Whitaker\n9998       Adin   m   Richards\n9999    Charley   m     Herman\n\n[10000 rows x 3 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>first_name</th>\n      <th>sex</th>\n      <th>last_name</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Eliana</td>\n      <td>f</td>\n      <td>Cardenas</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Meghann</td>\n      <td>f</td>\n      <td>Daniels</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Ada</td>\n      <td>f</td>\n      <td>Stevenson</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Elsa</td>\n      <td>f</td>\n      <td>Leblanc</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Avah</td>\n      <td>f</td>\n      <td>Lambert</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>Kasen</td>\n      <td>m</td>\n      <td>Barker</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>Camryn</td>\n      <td>m</td>\n      <td>Roberts</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>Henry</td>\n      <td>m</td>\n      <td>Whitaker</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>Adin</td>\n      <td>m</td>\n      <td>Richards</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>Charley</td>\n      <td>m</td>\n      <td>Herman</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 3 columns</p>\n</div>"
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# load the possible names\n",
+    "possible_names = pd.read_csv('./full_names.csv', index_col=0)\n",
+    "possible_names\n",
+    "# possible_names_list = (possible_names['first_name'] + ' ' + possible_names['last_name']).tolist()\n",
+    "# possible_names_list[:5]"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "     first_name sex  last_name         prompt_0                     prompt_1  \\\n0        Eliana   f   Cardenas  Eliana Cardenas  an image of Eliana Cardenas   \n1       Meghann   f    Daniels  Meghann Daniels  an image of Meghann Daniels   \n2           Ada   f  Stevenson    Ada Stevenson    an image of Ada Stevenson   \n3          Elsa   f    Leblanc     Elsa Leblanc     an image of Elsa Leblanc   \n4          Avah   f    Lambert     Avah Lambert     an image of Avah Lambert   \n...         ...  ..        ...              ...                          ...   \n9995      Kasen   m     Barker     Kasen Barker     an image of Kasen Barker   \n9996     Camryn   m    Roberts   Camryn Roberts   an image of Camryn Roberts   \n9997      Henry   m   Whitaker   Henry Whitaker   an image of Henry Whitaker   \n9998       Adin   m   Richards    Adin Richards    an image of Adin Richards   \n9999    Charley   m     Herman   Charley Herman   an image of Charley Herman   \n\n                        prompt_2                    prompt_3  \\\n0     a photo of Eliana Cardenas  Eliana Cardenas on a photo   \n1     a photo of Meghann Daniels  Meghann Daniels on a photo   \n2       a photo of Ada Stevenson    Ada Stevenson on a photo   \n3        a photo of Elsa Leblanc     Elsa Leblanc on a photo   \n4        a photo of Avah Lambert     Avah Lambert on a photo   \n...                          ...                         ...   \n9995     a photo of Kasen Barker     Kasen Barker on a photo   \n9996   a photo of Camryn Roberts   Camryn Roberts on a photo   \n9997   a photo of Henry Whitaker   Henry Whitaker on a photo   \n9998    a photo of Adin Richards    Adin Richards on a photo   \n9999   a photo of Charley Herman   Charley Herman on a photo   \n\n                                       prompt_4  \\\n0     a photo of a person named Eliana Cardenas   \n1     a photo of a person named Meghann Daniels   \n2       a photo of a person named Ada Stevenson   \n3        a photo of a person named Elsa Leblanc   \n4        a photo of a person named Avah Lambert   \n...                                         ...   \n9995     a photo of a person named Kasen Barker   \n9996   a photo of a person named Camryn Roberts   \n9997   a photo of a person named Henry Whitaker   \n9998    a photo of a person named Adin Richards   \n9999   a photo of a person named Charley Herman   \n\n                            prompt_5                     prompt_6  ...  \\\n0     a person named Eliana Cardenas  a man named Eliana Cardenas  ...   \n1     a person named Meghann Daniels  a man named Meghann Daniels  ...   \n2       a person named Ada Stevenson    a man named Ada Stevenson  ...   \n3        a person named Elsa Leblanc     a man named Elsa Leblanc  ...   \n4        a person named Avah Lambert     a man named Avah Lambert  ...   \n...                              ...                          ...  ...   \n9995     a person named Kasen Barker     a man named Kasen Barker  ...   \n9996   a person named Camryn Roberts   a man named Camryn Roberts  ...   \n9997   a person named Henry Whitaker   a man named Henry Whitaker  ...   \n9998    a person named Adin Richards    a man named Adin Richards  ...   \n9999   a person named Charley Herman   a man named Charley Herman  ...   \n\n                                     prompt_11              prompt_12  \\\n0     a photo of the celebrity Eliana Cardenas  actor Eliana Cardenas   \n1     a photo of the celebrity Meghann Daniels  actor Meghann Daniels   \n2       a photo of the celebrity Ada Stevenson    actor Ada Stevenson   \n3        a photo of the celebrity Elsa Leblanc     actor Elsa Leblanc   \n4        a photo of the celebrity Avah Lambert     actor Avah Lambert   \n...                                        ...                    ...   \n9995     a photo of the celebrity Kasen Barker     actor Kasen Barker   \n9996   a photo of the celebrity Camryn Roberts   actor Camryn Roberts   \n9997   a photo of the celebrity Henry Whitaker   actor Henry Whitaker   \n9998    a photo of the celebrity Adin Richards    actor Adin Richards   \n9999   a photo of the celebrity Charley Herman   actor Charley Herman   \n\n                    prompt_13                           prompt_14  \\\n0     actress Eliana Cardenas  a colored photo of Eliana Cardenas   \n1     actress Meghann Daniels  a colored photo of Meghann Daniels   \n2       actress Ada Stevenson    a colored photo of Ada Stevenson   \n3        actress Elsa Leblanc     a colored photo of Elsa Leblanc   \n4        actress Avah Lambert     a colored photo of Avah Lambert   \n...                       ...                                 ...   \n9995     actress Kasen Barker     a colored photo of Kasen Barker   \n9996   actress Camryn Roberts   a colored photo of Camryn Roberts   \n9997   actress Henry Whitaker   a colored photo of Henry Whitaker   \n9998    actress Adin Richards    a colored photo of Adin Richards   \n9999   actress Charley Herman   a colored photo of Charley Herman   \n\n                                       prompt_15  \\\n0     a black and white photo of Eliana Cardenas   \n1     a black and white photo of Meghann Daniels   \n2       a black and white photo of Ada Stevenson   \n3        a black and white photo of Elsa Leblanc   \n4        a black and white photo of Avah Lambert   \n...                                          ...   \n9995     a black and white photo of Kasen Barker   \n9996   a black and white photo of Camryn Roberts   \n9997   a black and white photo of Henry Whitaker   \n9998    a black and white photo of Adin Richards   \n9999   a black and white photo of Charley Herman   \n\n                            prompt_16                           prompt_17  \\\n0     a cool photo of Eliana Cardenas  a cropped photo of Eliana Cardenas   \n1     a cool photo of Meghann Daniels  a cropped photo of Meghann Daniels   \n2       a cool photo of Ada Stevenson    a cropped photo of Ada Stevenson   \n3        a cool photo of Elsa Leblanc     a cropped photo of Elsa Leblanc   \n4        a cool photo of Avah Lambert     a cropped photo of Avah Lambert   \n...                               ...                                 ...   \n9995     a cool photo of Kasen Barker     a cropped photo of Kasen Barker   \n9996   a cool photo of Camryn Roberts   a cropped photo of Camryn Roberts   \n9997   a cool photo of Henry Whitaker   a cropped photo of Henry Whitaker   \n9998    a cool photo of Adin Richards    a cropped photo of Adin Richards   \n9999   a cool photo of Charley Herman   a cropped photo of Charley Herman   \n\n                               prompt_18                  prompt_19  \\\n0     a cropped image of Eliana Cardenas  Eliana Cardenas in a suit   \n1     a cropped image of Meghann Daniels  Meghann Daniels in a suit   \n2       a cropped image of Ada Stevenson    Ada Stevenson in a suit   \n3        a cropped image of Elsa Leblanc     Elsa Leblanc in a suit   \n4        a cropped image of Avah Lambert     Avah Lambert in a suit   \n...                                  ...                        ...   \n9995     a cropped image of Kasen Barker     Kasen Barker in a suit   \n9996   a cropped image of Camryn Roberts   Camryn Roberts in a suit   \n9997   a cropped image of Henry Whitaker   Henry Whitaker in a suit   \n9998    a cropped image of Adin Richards    Adin Richards in a suit   \n9999   a cropped image of Charley Herman   Charley Herman in a suit   \n\n                       prompt_20  \n0     Eliana Cardenas in a dress  \n1     Meghann Daniels in a dress  \n2       Ada Stevenson in a dress  \n3        Elsa Leblanc in a dress  \n4        Avah Lambert in a dress  \n...                          ...  \n9995     Kasen Barker in a dress  \n9996   Camryn Roberts in a dress  \n9997   Henry Whitaker in a dress  \n9998    Adin Richards in a dress  \n9999   Charley Herman in a dress  \n\n[10000 rows x 24 columns]",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>first_name</th>\n      <th>sex</th>\n      <th>last_name</th>\n      <th>prompt_0</th>\n      <th>prompt_1</th>\n      <th>prompt_2</th>\n      <th>prompt_3</th>\n      <th>prompt_4</th>\n      <th>prompt_5</th>\n      <th>prompt_6</th>\n      <th>...</th>\n      <th>prompt_11</th>\n      <th>prompt_12</th>\n      <th>prompt_13</th>\n      <th>prompt_14</th>\n      <th>prompt_15</th>\n      <th>prompt_16</th>\n      <th>prompt_17</th>\n      <th>prompt_18</th>\n      <th>prompt_19</th>\n      <th>prompt_20</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Eliana</td>\n      <td>f</td>\n      <td>Cardenas</td>\n      <td>Eliana Cardenas</td>\n      <td>an image of Eliana Cardenas</td>\n      <td>a photo of Eliana Cardenas</td>\n      <td>Eliana Cardenas on a photo</td>\n      <td>a photo of a person named Eliana Cardenas</td>\n      <td>a person named Eliana Cardenas</td>\n      <td>a man named Eliana Cardenas</td>\n      <td>...</td>\n      <td>a photo of the celebrity Eliana Cardenas</td>\n      <td>actor Eliana Cardenas</td>\n      <td>actress Eliana Cardenas</td>\n      <td>a colored photo of Eliana Cardenas</td>\n      <td>a black and white photo of Eliana Cardenas</td>\n      <td>a cool photo of Eliana Cardenas</td>\n      <td>a cropped photo of Eliana Cardenas</td>\n      <td>a cropped image of Eliana Cardenas</td>\n      <td>Eliana Cardenas in a suit</td>\n      <td>Eliana Cardenas in a dress</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Meghann</td>\n      <td>f</td>\n      <td>Daniels</td>\n      <td>Meghann Daniels</td>\n      <td>an image of Meghann Daniels</td>\n      <td>a photo of Meghann Daniels</td>\n      <td>Meghann Daniels on a photo</td>\n      <td>a photo of a person named Meghann Daniels</td>\n      <td>a person named Meghann Daniels</td>\n      <td>a man named Meghann Daniels</td>\n      <td>...</td>\n      <td>a photo of the celebrity Meghann Daniels</td>\n      <td>actor Meghann Daniels</td>\n      <td>actress Meghann Daniels</td>\n      <td>a colored photo of Meghann Daniels</td>\n      <td>a black and white photo of Meghann Daniels</td>\n      <td>a cool photo of Meghann Daniels</td>\n      <td>a cropped photo of Meghann Daniels</td>\n      <td>a cropped image of Meghann Daniels</td>\n      <td>Meghann Daniels in a suit</td>\n      <td>Meghann Daniels in a dress</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Ada</td>\n      <td>f</td>\n      <td>Stevenson</td>\n      <td>Ada Stevenson</td>\n      <td>an image of Ada Stevenson</td>\n      <td>a photo of Ada Stevenson</td>\n      <td>Ada Stevenson on a photo</td>\n      <td>a photo of a person named Ada Stevenson</td>\n      <td>a person named Ada Stevenson</td>\n      <td>a man named Ada Stevenson</td>\n      <td>...</td>\n      <td>a photo of the celebrity Ada Stevenson</td>\n      <td>actor Ada Stevenson</td>\n      <td>actress Ada Stevenson</td>\n      <td>a colored photo of Ada Stevenson</td>\n      <td>a black and white photo of Ada Stevenson</td>\n      <td>a cool photo of Ada Stevenson</td>\n      <td>a cropped photo of Ada Stevenson</td>\n      <td>a cropped image of Ada Stevenson</td>\n      <td>Ada Stevenson in a suit</td>\n      <td>Ada Stevenson in a dress</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Elsa</td>\n      <td>f</td>\n      <td>Leblanc</td>\n      <td>Elsa Leblanc</td>\n      <td>an image of Elsa Leblanc</td>\n      <td>a photo of Elsa Leblanc</td>\n      <td>Elsa Leblanc on a photo</td>\n      <td>a photo of a person named Elsa Leblanc</td>\n      <td>a person named Elsa Leblanc</td>\n      <td>a man named Elsa Leblanc</td>\n      <td>...</td>\n      <td>a photo of the celebrity Elsa Leblanc</td>\n      <td>actor Elsa Leblanc</td>\n      <td>actress Elsa Leblanc</td>\n      <td>a colored photo of Elsa Leblanc</td>\n      <td>a black and white photo of Elsa Leblanc</td>\n      <td>a cool photo of Elsa Leblanc</td>\n      <td>a cropped photo of Elsa Leblanc</td>\n      <td>a cropped image of Elsa Leblanc</td>\n      <td>Elsa Leblanc in a suit</td>\n      <td>Elsa Leblanc in a dress</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Avah</td>\n      <td>f</td>\n      <td>Lambert</td>\n      <td>Avah Lambert</td>\n      <td>an image of Avah Lambert</td>\n      <td>a photo of Avah Lambert</td>\n      <td>Avah Lambert on a photo</td>\n      <td>a photo of a person named Avah Lambert</td>\n      <td>a person named Avah Lambert</td>\n      <td>a man named Avah Lambert</td>\n      <td>...</td>\n      <td>a photo of the celebrity Avah Lambert</td>\n      <td>actor Avah Lambert</td>\n      <td>actress Avah Lambert</td>\n      <td>a colored photo of Avah Lambert</td>\n      <td>a black and white photo of Avah Lambert</td>\n      <td>a cool photo of Avah Lambert</td>\n      <td>a cropped photo of Avah Lambert</td>\n      <td>a cropped image of Avah Lambert</td>\n      <td>Avah Lambert in a suit</td>\n      <td>Avah Lambert in a dress</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>Kasen</td>\n      <td>m</td>\n      <td>Barker</td>\n      <td>Kasen Barker</td>\n      <td>an image of Kasen Barker</td>\n      <td>a photo of Kasen Barker</td>\n      <td>Kasen Barker on a photo</td>\n      <td>a photo of a person named Kasen Barker</td>\n      <td>a person named Kasen Barker</td>\n      <td>a man named Kasen Barker</td>\n      <td>...</td>\n      <td>a photo of the celebrity Kasen Barker</td>\n      <td>actor Kasen Barker</td>\n      <td>actress Kasen Barker</td>\n      <td>a colored photo of Kasen Barker</td>\n      <td>a black and white photo of Kasen Barker</td>\n      <td>a cool photo of Kasen Barker</td>\n      <td>a cropped photo of Kasen Barker</td>\n      <td>a cropped image of Kasen Barker</td>\n      <td>Kasen Barker in a suit</td>\n      <td>Kasen Barker in a dress</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>Camryn</td>\n      <td>m</td>\n      <td>Roberts</td>\n      <td>Camryn Roberts</td>\n      <td>an image of Camryn Roberts</td>\n      <td>a photo of Camryn Roberts</td>\n      <td>Camryn Roberts on a photo</td>\n      <td>a photo of a person named Camryn Roberts</td>\n      <td>a person named Camryn Roberts</td>\n      <td>a man named Camryn Roberts</td>\n      <td>...</td>\n      <td>a photo of the celebrity Camryn Roberts</td>\n      <td>actor Camryn Roberts</td>\n      <td>actress Camryn Roberts</td>\n      <td>a colored photo of Camryn Roberts</td>\n      <td>a black and white photo of Camryn Roberts</td>\n      <td>a cool photo of Camryn Roberts</td>\n      <td>a cropped photo of Camryn Roberts</td>\n      <td>a cropped image of Camryn Roberts</td>\n      <td>Camryn Roberts in a suit</td>\n      <td>Camryn Roberts in a dress</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>Henry</td>\n      <td>m</td>\n      <td>Whitaker</td>\n      <td>Henry Whitaker</td>\n      <td>an image of Henry Whitaker</td>\n      <td>a photo of Henry Whitaker</td>\n      <td>Henry Whitaker on a photo</td>\n      <td>a photo of a person named Henry Whitaker</td>\n      <td>a person named Henry Whitaker</td>\n      <td>a man named Henry Whitaker</td>\n      <td>...</td>\n      <td>a photo of the celebrity Henry Whitaker</td>\n      <td>actor Henry Whitaker</td>\n      <td>actress Henry Whitaker</td>\n      <td>a colored photo of Henry Whitaker</td>\n      <td>a black and white photo of Henry Whitaker</td>\n      <td>a cool photo of Henry Whitaker</td>\n      <td>a cropped photo of Henry Whitaker</td>\n      <td>a cropped image of Henry Whitaker</td>\n      <td>Henry Whitaker in a suit</td>\n      <td>Henry Whitaker in a dress</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>Adin</td>\n      <td>m</td>\n      <td>Richards</td>\n      <td>Adin Richards</td>\n      <td>an image of Adin Richards</td>\n      <td>a photo of Adin Richards</td>\n      <td>Adin Richards on a photo</td>\n      <td>a photo of a person named Adin Richards</td>\n      <td>a person named Adin Richards</td>\n      <td>a man named Adin Richards</td>\n      <td>...</td>\n      <td>a photo of the celebrity Adin Richards</td>\n      <td>actor Adin Richards</td>\n      <td>actress Adin Richards</td>\n      <td>a colored photo of Adin Richards</td>\n      <td>a black and white photo of Adin Richards</td>\n      <td>a cool photo of Adin Richards</td>\n      <td>a cropped photo of Adin Richards</td>\n      <td>a cropped image of Adin Richards</td>\n      <td>Adin Richards in a suit</td>\n      <td>Adin Richards in a dress</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>Charley</td>\n      <td>m</td>\n      <td>Herman</td>\n      <td>Charley Herman</td>\n      <td>an image of Charley Herman</td>\n      <td>a photo of Charley Herman</td>\n      <td>Charley Herman on a photo</td>\n      <td>a photo of a person named Charley Herman</td>\n      <td>a person named Charley Herman</td>\n      <td>a man named Charley Herman</td>\n      <td>...</td>\n      <td>a photo of the celebrity Charley Herman</td>\n      <td>actor Charley Herman</td>\n      <td>actress Charley Herman</td>\n      <td>a colored photo of Charley Herman</td>\n      <td>a black and white photo of Charley Herman</td>\n      <td>a cool photo of Charley Herman</td>\n      <td>a cropped photo of Charley Herman</td>\n      <td>a cropped image of Charley Herman</td>\n      <td>Charley Herman in a suit</td>\n      <td>Charley Herman in a dress</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 24 columns</p>\n</div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# populate the prompts with the possible names\n",
+    "prompts = []\n",
+    "for idx, row in possible_names.iterrows():\n",
+    "    df_dict = row.to_dict()\n",
+    "    name = f'{row[\"first_name\"]} {row[\"last_name\"]}'\n",
+    "    for prompt_idx, prompt in enumerate(PROMPTS):\n",
+    "        df_dict[f'prompt_{prompt_idx}'] = prompt.format(name)\n",
+    "    prompts.append(df_dict)\n",
+    "prompts = pd.DataFrame(prompts)\n",
+    "prompts"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "outputs": [],
+   "source": [
+    "label_context_vecs = []\n",
+    "for i in range(len(PROMPTS)):\n",
+    "    context = open_clip.tokenize(prompts[f'prompt_{i}'].to_numpy())\n",
+    "    label_context_vecs.append(context)\n",
+    "label_context_vecs = torch.stack(label_context_vecs)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "Calculating Text Embeddings:   0%|          | 0/210 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "4267d43b498f481db5cbf7e709c9ace3"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "Calculating Text Embeddings:   0%|          | 0/210 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "34a21714ab4d42b2beaa3024bcdd8fdd"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": "Calculating Text Embeddings:   0%|          | 0/210 [00:00<?, ?it/s]",
+      "application/vnd.jupyter.widget-view+json": {
+       "version_major": 2,
+       "version_minor": 0,
+       "model_id": "3278ad478d7d455da8b03d954fbc4558"
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "label_context_vecs = label_context_vecs.to(device)\n",
+    "\n",
+    "text_embeddings_per_model = {}\n",
+    "for model_name, model in models.items():\n",
+    "    model = model.to(device)\n",
+    "    text_embeddings = get_text_embeddings(model, label_context_vecs, use_tqdm=True, context_batchsize=1_000)\n",
+    "    text_embeddings_per_model[model_name] = text_embeddings\n",
+    "    model = model.cpu()\n",
+    "\n",
+    "label_context_vecs = label_context_vecs.cpu()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "outputs": [],
+   "source": [
+    "# save the calculated embeddings to a file\n",
+    "if not os.path.exists('./prompt_text_embeddings'):\n",
+    "    os.makedirs('./prompt_text_embeddings')"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "outputs": [],
+   "source": [
+    "for model_name, _ in models.items():\n",
+    "    torch.save(\n",
+    "        text_embeddings_per_model[model_name],\n",
+    "        f'./prompt_text_embeddings/{model_name}_prompt_text_embeddings.pt'\n",
+    "    )"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

download_example_images.py

@@ -0,0 +1,42 @@
+import os
+import urllib.request
+from tqdm import tqdm
+from PIL import Image
+
+
+def read_actor_files(folder_path):
+    urls = {}
+    for file in os.listdir(folder_path):
+        if not file.endswith('.txt'):
+            continue
+
+        file_name_without_ext = os.path.splitext(file)[0]
+        with open(os.path.join(folder_path, file)) as text_file:
+            lines = text_file.readlines()
+            lines = [line.rstrip() for line in lines]
+
+        urls[file_name_without_ext] = lines
+
+    return urls
+
+
+def save_images_to_folder(folder_path, url_dict):
+    url_opener = urllib.request.URLopener()
+    url_opener.addheader('User-Agent',
+                         'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36')
+
+    for name, url_list in tqdm(url_dict.items()):
+        base_folder = os.path.join(folder_path, name)
+        if os.path.exists(base_folder):
+            print(f'The image folder {base_folder} already exists. Skipping folder.')
+            continue
+        os.makedirs(base_folder)
+        for i, url in tqdm(enumerate(url_list), desc=name, leave=False):
+            url = urllib.parse.quote(url, safe='://?=&(),%+')
+            img_file_path = os.path.join(base_folder, f'{name}_{i}.jpg')
+            url_opener.retrieve(url, img_file_path)
+
+            # open the image and resize it
+            img = Image.open(img_file_path)
+            img.thumbnail((1024, 1024))
+            img.save(img_file_path)

laion_membership_occurence_count.csv

@@ -0,0 +1,547 @@
+,name,count,membership,bin
+0,Malu Leicher,0,non_member,"[0, 25)"
+1,Bernhard Hoëcker,0,non_member,"[0, 25)"
+2,Adrianne León,0,non_member,"[0, 25)"
+3,Harriet Herbig-Matten,0,non_member,"[0, 25)"
+4,Jenilee Harrison,1,member,"[0, 25)"
+5,Joanna García,1,member,"[0, 25)"
+6,Sabine Vitua,1,member,"[0, 25)"
+7,Bettina Lamprecht,2,member,"[0, 25)"
+8,Guido Cantz,2,member,"[0, 25)"
+9,Taylor Atelian,2,member,"[0, 25)"
+10,Jörg Pilawa,2,member,"[0, 25)"
+11,Max Giermann,4,member,"[0, 25)"
+12,Mackenzie Aladjem,4,member,"[0, 25)"
+13,Johannes B. Kerner,4,member,"[0, 25)"
+14,Erin Chambers,6,member,"[0, 25)"
+15,Bastian Pastewka,6,member,"[0, 25)"
+16,Staci Keanan,6,member,"[0, 25)"
+17,Kai Pflaume,7,member,"[0, 25)"
+18,Freddy Rodríguez,7,member,"[0, 25)"
+19,Annette Frier,10,member,"[0, 25)"
+20,Christina Bennett Lind,10,member,"[0, 25)"
+21,Jessica Leccia,11,member,"[0, 25)"
+22,Valerie Cruz,11,member,"[0, 25)"
+23,Lourdes Benedicto,14,member,"[0, 25)"
+24,Shannon Kane,14,member,"[0, 25)"
+25,Alice Dwyer,15,member,"[0, 25)"
+26,Andrea Bogart,15,member,"[0, 25)"
+27,Florencia Lozano,19,member,"[0, 25)"
+28,Kimberly Matula,24,member,"[0, 25)"
+29,Ilene Kristen,25,member,"[25, 50)"
+30,Natalia Livingston,27,member,"[25, 50)"
+31,Lexi Ainsworth,29,member,"[25, 50)"
+32,Molly Burnett,31,member,"[25, 50)"
+33,Julie Marie Berman,32,member,"[25, 50)"
+34,Carla Gallo,32,member,"[25, 50)"
+35,Olivia d'Abo,34,member,"[25, 50)"
+36,Jonathan Sadowski,34,member,"[25, 50)"
+37,Wanda De Jesus,34,member,"[25, 50)"
+38,Carolin Kebekus,36,member,"[25, 50)"
+39,Michael Kessler,36,member,"[25, 50)"
+40,Veronica Hamel,37,member,"[25, 50)"
+41,Patricia Kalember,37,member,"[25, 50)"
+42,Jodi Long,38,member,"[25, 50)"
+43,Crystal Chappell,38,member,"[25, 50)"
+44,Audrey Landers,38,member,"[25, 50)"
+45,Nicole de Boer,40,member,"[25, 50)"
+46,T. J. Thyne,41,member,"[25, 50)"
+47,Natalie Hall,41,member,"[25, 50)"
+48,Michael Landes,42,member,"[25, 50)"
+49,Bobbie Eakes,46,member,"[25, 50)"
+50,Judi Evans,51,member,"[50, 75)"
+51,Brooke Langton,57,member,"[50, 75)"
+52,Lisa LoCicero,57,member,"[50, 75)"
+53,Mary Crosby,58,member,"[50, 75)"
+54,Chase Masterson,59,member,"[50, 75)"
+55,Erin Cummings,61,member,"[50, 75)"
+56,Jake Weber,62,member,"[50, 75)"
+57,Arnold Vosloo,64,member,"[50, 75)"
+58,Peggy McCay,65,member,"[50, 75)"
+59,Crystal Bernard,67,member,"[50, 75)"
+60,Barbara Schöneberger,68,member,"[50, 75)"
+61,Melissa Archer,69,member,"[50, 75)"
+62,Shelley Hack,69,member,"[50, 75)"
+63,Jill Eikenberry,72,member,"[50, 75)"
+64,Kimberlin Brown,73,member,"[50, 75)"
+65,Lecy Goranson,77,member,"[75, 100)"
+66,Jackee Harry,77,member,"[75, 100)"
+67,Tracey Gold,78,member,"[75, 100)"
+68,Kristen Alderson,78,member,"[75, 100)"
+69,Rebecca Herbst,84,member,"[75, 100)"
+70,Joanna Kerns,85,member,"[75, 100)"
+71,Joyce DeWitt,88,member,"[75, 100)"
+72,Lesley-Anne Down,90,member,"[75, 100)"
+73,Jamie Luner,90,member,"[75, 100)"
+74,Adrienne Frantz,90,member,"[75, 100)"
+75,Kimberly McCullough,94,member,"[75, 100)"
+76,Farah Fath,95,member,"[75, 100)"
+77,Pamela Sue Martin,96,member,"[75, 100)"
+78,Ellen Greene,98,member,"[75, 100)"
+79,Lindsay Hartley,98,member,"[75, 100)"
+80,Amanda Bearse,100,member,"[100, 125)"
+81,Tempestt Bledsoe,102,member,"[100, 125)"
+82,Alley Mills,104,member,"[100, 125)"
+83,Jennifer Gareis,107,member,"[100, 125)"
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